About

Rakshith Vasudev is a highly skilled Systems Development Engineer at Dell Technologies, with a Master's degree in Software Engineering. He is an expert in data science, machine learning, deep learning, and natural language processing, and possesses a wide range of technical skills including Python, Java, Numpy, Pandas, Tensorflow, Keras, Pytorch, Matplotlib, Seaborn, ggplot, Scikit-Learn, Gensim, NLTK, Apache Spark, SQL, Tableau, Scrapy, xgboost, lightGBM, and Excel. Rakshith has extensive experience in collecting, cleaning, organizing, and building machine learning and deep learning pipelines. He possesses conceptual skills in multivariate calculus, linear algebra, data visualization and communication, data wrangling, GLM's, Bayesian methods, hypothesis testing, feature engineering, data mining, distributions, probability, LSTMS, RNNS, CNNS, A/B testing, confidence intervals, sampling, regularization, ANOVA, chi-square, correlation, classification, ROC, regression, LightGBM, latent Dirichlet allocation, PCA, LASSO, ridge, elastic net, decision trees, random forest, neural networks, Word2Vec, TF-IDF, bag of words, and more. Currently, Rakshith is working as an AI Software Engineer at Ford, where he is involved in enabling large-scale AI-based solution requests that come to the HPC/AI workloads team. He is also supporting projects related to computer vision, natural language processing, and supporting data scientists/researchers to be able to run their models that are containerized in an on-prem HPC setting for the internal Ford ML platform. Additionally, he is automating benchmarking to track hardware and software performance difference. Prior to Ford, Rakshith worked at Dell as a Systems Development Engineer in the AI team and as an AI Software Engineer. In these roles, he researched, validated, and enabled AI workloads/datascience, ran deep learning use cases, explored Apache Spark on Kubernetes, monitored AI workloads using Grafana and Prometheus to performance tune training process to reduce time to solution, monitored GPU consumption using Nvidia Data center GPU Manager (DCGM), deployed Bare Metal Kubernetes, deployed and monitored Spark ML workloads on K8s, built distributed deep learning models using multiple Nvidia GPUs in an on-prem HPC world at HPC AI Innovation Lab, and more. Raksh